High-throughput methods for miniaturization and automation of monoclonal antibody purification processes

In the last decade, high-throughput downstream process development techniques have entered the biopharmaceutical industry. As chromatography is the standard downstream purification method, several high-throughput chromatographic methods have been developed and applied including miniaturized chromatographic columns for utilization on liquid handling stations. These columns were used to setup a complete downstream process on a liquid handling station for the first time. In this article, a monoclonal antibody process was established in lab-scale and miniaturized afterwards. The scale-down methodology is presented and discussed. Liquid handling in miniaturized single and multicolumn processes was improved and applicability was demonstrated by volume balances. The challenges of absorption measurement are discussed and strategies were shown to improve volume balances and mass balances in 96-well microtiter plates. The feasibility of miniaturizing a complete downstream process was shown. In the future, analytical bottlenecks should be addressed to gain the full benefit from miniaturized complete process development.     

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application – SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary “Manual Initialize” and “Hand Draw” tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.     

高通量DNA甲基化数据的处理和分析方法

DNA甲基化作为一种表观遗传学修饰,在调控基因表达、X染色体失活、印记基因等方面都发挥着重要的作用.不同的DNA甲基化的预处理方法结合二代测序产生了大量的高通量甲基化数据,这些数据的存储、处理和分析是当前亟需解决的问题.在本文中,总结了目前存在的三种高通量DNA甲基化检测技术（限制性内切酶法,亲和纯化法,重亚硫酸盐转换法）,以及针对这些技术产生的高通量数据开发的存储、处理和分析工具.另外,还注重介绍了单碱基水平的DNA甲基化检测技术,BS-Seq的测序原理、数据处理流程以及后续的分析工具.     

Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.     

A homogeneous cell-based assay to identify N-linked carbohydrate processing inhibitors

Malignant transformation is accompanied by altered cell surface glycosylation. N-Linked oligosaccharides carrying beta1-6GlcNAc branches are associated with tumor invasion and metastasis. Therefore, compounds that can enter cells and block biosynthesis of beta1-6GlcNAc-branched glycans without overt cytotoxicity are potential anticancer agents. We have developed a homogeneous cell-based assay for detection of such compounds. The method enables identification of agents that block beta1-6GlcNAc-branched glycan expression after incubation for 16-20 h with MDAY-D2 tumor cells, thereby protecting the cells from the subsequent addition of leukoagglutinin, a cytotoxic plant lectin. We observed that MDAY-D2 cell number is directly proportional to the level of endogenous alkaline phosphatase activity measured spectrophotometrically in cultures after the addition of substrate. The alkaline phosphatase assay was capable of detecting as few as 1,500 cells. The assay was readily adapted for high-throughput screening as reagent costs are low and no cell harvesting and washing steps are required. Under high-throughput operating conditions, the coefficient of variation for controls was found to be 4.2%. The results suggest that measurement of alkaline phosphatase in this cell assay format may be adapted for wider applications in high-throughput screenings for compounds that relieve cells from other growth inhibitors.     

From single cells to deep phenotypes in cancer

In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.     

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays.     

Noninvasive fluorescent imaging reliably estimates biomass in vivo

Whole-body optical imaging of small animals has emerged as a powerful, user friendly, and high-throughput tool for assaying molecular and cellular processes as they occur in vivo. As with any imaging method, the utility of such technology relies on its ability to provide quantitative, biologically meaningful information about the physiologic or pathologic process of interest. Here we used an animal tumor model to evaluate the extent of correlation between noninvasively measured fluorescence and more traditional measurements of biomass (tumor volume and tumor weight). C57/BL6 mice were injected subcutaneously with murine colon adenocarcinoma cells that were engineered to express GFP. Serial measurements of fluorescence intensities were performed with a macroscopic in vivo fluorescence system. The progressive increases in intensity correlated strongly with growth in tumor volume, as determined by caliper measurements (R2 = 0.99). A more stringent correlation was found between fluorescence intensity and tumor weight (R2 = 0.97) than between volume and weight (R2 = 0.89). In a treatment experiment using tumor necrosis factor-alpha, fluorescence intensity (but not tumor volume) was able to differentiate between treated and control groups on day 1 post-treatment. These results validate the ability of noninvasive fluorescent imaging to quantify the number of viable, fluorescent cells in vivo.     

Stromal interaction molecule 1 promotes tumor growth in Esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a disease with poor prognosis which urgently is in need of effective prognostic marker. To discover novel prognostic protein marker for ESCC, we applied a high-throughput monoclonal antibody microarray to compare tumor and adjacent non-tumor tissues from ESCC patients. Antibody #ESmAb270 was consistent higher expressed in tumors and it was identified via mass spectrometry to be stromal interaction molecule 1 (STIM1). STIM1 H scores in tumor tissues were significantly up-regulated in esophageal tumor tissues compared to non-tumor tissues in 105 ESCC patients. We also observed that high STIM1 expression was correlated with advanced tumor grade and poor prognosis of ESCC. In addition, attenuation of STIM1 by siRNA or chemical inhibitors significantly inhibited cell viability and migration of ESCC cells. Evidence from high-throughput monoclonal antibody microarray, IHC microarray with associated survival data and functional analysis show that STIM1 is an unfavorable prognostic biomarker in ESCC.     

High-throughput quantification of hydroxyproline for determination of collagen

An accurate and high-throughput assay for collagen is essential for collagen research and development of collagen products. Hydroxyproline is routinely assayed to provide a measurement for collagen quantification. The time required for sample preparation using acid hydrolysis and neutralization prior to assay is what limits the current method for determining hydroxyproline. This work describes the conditions of alkali hydrolysis that, when combined with the colorimetric assay defined by Woessner, provide a high-throughput, accurate method for the measurement of hydroxyproline.     

High-throughput bioinformatics with the Cyrille2 pipeline system

Background  

Modern omics research involves the application of high-throughput technologies that generate vast volumes of data. These data need to be pre-processed, analyzed and integrated with existing knowledge through the use of diverse sets of software tools, models and databases. The analyses are often interdependent and chained together to form complex workflows or pipelines. Given the volume of the data used and the multitude of computational resources available, specialized pipeline software is required to make high-throughput analysis of large-scale omics datasets feasible.     

High-throughput sequence alignment using Graphics Processing Units

Background  

The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies.     

Application of the BD ACTOne technology for the high-throughput screening of Gs-coupled receptor antagonists

A novel technology for monitoring the changes of 3,'5'-adenosine cyclic monophosphate (cAMP) in live cells suitable for drug screening relies on the use of cyclic nucleotide-gated channels as biosensors coexpressed with the appropriate target receptor. The technique (termed BD ACTOne) offers measurement of cAMP-dependent calcium influx or membrane depolarization with conventional fluorescent methods both in kinetic and in endpoint modes, optimal for high-throughput and subsequent compound screening. The utility of the technique is reported here based on assay development and high-throughput screening for small-molecule antagonists of the peptide parathyroid hormone 2 receptor (PTH2R). The dual-signaling properties of the receptor were retained in the recombinant system, and the observed pharmacological profile corresponded to data from radiolabeled cAMP determination. The membrane-potential-based high-throughput assay produced reproducible actives and led to the identification of several chemical scaffolds with potential utility as PTH2R ligands.     

Automated analysis and classification of infected macrophages using bright-field amplitude contrast data

This article presents a methodology for acquisition and analysis of bright-field amplitude contrast image data in high-throughput screening (HTS) for the measurement of cell density, cell viability, and classification of individual cells into phenotypic classes. We present a robust image analysis pipeline, where the original data are subjected to image standardization, image enhancement, and segmentation by region growing. This work develops new imaging and analysis techniques for cell analysis in HTS and successfully addresses a particular need for direct measurement of cell density and other features without using dyes.     

Quantitative analysis of microRNAs in tissue microarrays by in situ hybridization

MicroRNAs (miRNAs) have emerged as key regulators in the pathogenesis of cancers where they can act as either oncogenes or tumor suppressors. Most miRNA measurement methods require total RNA extracts which lack critical spatial information and present challenges for standardization. We have developed and validated a method for the quantitative analysis of miRNA expression by in situ hybridization (ISH) allowing for the direct assessment of tumor epithelial expression of miRNAs. This co-localization based approach (called qISH) utilizes DAPI and cytokeratin immunofluorescence to establish subcellular compartments in the tumor epithelia, then multiplexed with the miRNA ISH, allows for quantitative measurement of miRNA expression within these compartments. We use this approach to assess miR-21, miR-92a, miR-34a, and miR-221 expression in 473 breast cancer specimens on tissue microarrays. We found that miR-221 levels are prognostic in breast cancer illustrating the high-throughput method and confirming that miRNAs can be valuable biomarkers in cancer. Furthermore, in applying this method we found that the inverse relationship between miRNAs and proposed target proteins is difficult to discern in large population cohorts. Our method demonstrates an approach for large cohort, tissue microarray-based assessment of miRNA expression.     

Classical mathematical models for prediction of response to chemotherapy and immunotherapy

Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real world-data of human patients. In this study, we fitted a range of differential equation models to tumor volume measurements of patients undergoing chemotherapy or cancer immunotherapy for solid tumors. We used a large dataset of 1472 patients with three or more measurements per target lesion, of which 652 patients had six or more data points. We show that the early treatment response shows only moderate correlation with the final treatment response, demonstrating the need for nuanced models. We then perform a head-to-head comparison of six classical models which are widely used in the field: the Exponential, Logistic, Classic Bertalanffy, General Bertalanffy, Classic Gompertz and General Gompertz model. Several models provide a good fit to tumor volume measurements, with the Gompertz model providing the best balance between goodness of fit and number of parameters. Similarly, when fitting to early treatment data, the general Bertalanffy and Gompertz models yield the lowest mean absolute error to forecasted data, indicating that these models could potentially be effective at predicting treatment outcome. In summary, we provide a quantitative benchmark for classical textbook models and state-of-the art models of human tumor growth. We publicly release an anonymized version of our original data, providing the first benchmark set of human tumor growth data for evaluation of mathematical models.     

Isotopic profiling of ¹³C-labeled biological samples by two-dimensional heteronuclear J-resolved nuclear magnetic resonance spectroscopy

The use of two-dimensional heteronuclear J-resolved (2D H-JRES) nuclear magnetic resonance (NMR) spectroscopy for fast and reliable measurement of isotopic patterns from 13C-enriched compounds resulting from carbon labeling experiments was evaluated. Its use with biological samples of increasing complexity showed that 2D H-JRES spectroscopy is suitable for high-throughput isotopic profiling of any kind of labeled samples. Moreover, the method enabled accurate quantification of 13C enrichments and, thus, can be used for metabolic flux analysis. The excellent trade-off between reduced experimental time and the number of measurable isotopic data makes 2D H-JRES NMR a promising approach for high-throughput flux analysis of samples of intermediate complexity.     

Linear fuzzy gene network models obtained from microarray data by exhaustive search

Background  

Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling.